Tapan Gandhi

Autism as a disorder of prediction

Significance Autism is characterized by diverse behavioral traits. Guided by theoretical considerations and empirical data, this paper develops the hypothesis that many of autisms salient traits may be manifestations of an underlying impairment in predictive abilities. This impairment renders an otherwise orderly world to be experienced as a capriciously “magical” one. The hypothesis elucidates the information-processing roots of autism and, thereby, can aid the identification of neural structures likely to be differentially affected. Behavioral and neural measures of prediction might serve as early assays of predictive abilities in infants, and serve as useful tools in intervention design and in monitoring their effectiveness. The hypothesis also points to avenues for further research to determine molecular and circuit-level causal underpinnings of predictive impairments. A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly “magical” world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one’s ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.

Expert model for detection of epileptic activity in EEG signature

Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and high accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet transform (DWT) and energy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN) for classification. Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till the sixth-level using DWT. Approximate energy (EDA) values of the wavelet coefficients at all nodes of the down sampled tree were used as a feature vector to characterize the predictability of the epileptic activity within the records of EEG data. In order to demonstrate the classification accuracy of the proposed probabilistic neural network, tenfold cross-validation was implemented in the expert model. Clinical EEG data recorded from normal as well as epileptic subjects were used to test the performance of this new scheme. It was found that with the proposed scheme, the detection is 99.33% accurate with sensitivity and specificity as 99.6% and 99%, respectively. The proposed model can be widely used in developing countries where there is an acute shortage of trained neurologist.

Discrete harmony search based expert model for epileptic seizure detection in electroencephalography

Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and highest accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet packet transform (DWT) with energy, entropy, standard deviation, mean, kurtosis, skewness and entropy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN). Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till sixth-level using DWT packet. Discrete harmony search with modified differential operator was used to select the optimal features out of all above mentioned statistical and non-statistical parameters. In order to demonstrate the efficacy of the proposed algorithm for classification purpose using PNN, we have implemented 10-fold cross validation. Clinical EEG data recorded from normal as well as epileptic subjects are used to test the performance of this new scheme. It is found that the detection rate is 100% accurate with same level of sensitivity and specificity.

A novel robust diagnostic model to detect seizures in electroencephalography

A robust method is proposed for efficient detection of seizures in EEG.Dual tree-complex wavelet transform is used for feature extraction.General regression neural network is employed to classify extracted features.The proposed technique is giving ceiling level performance.The model can be used for fast and accurate diagnosis of epilepsy. Identifying seizure patterns in complex electroencephalography (EEG) through visual inspection is often challenging, time-consuming and prone to errors. These problems have motivated the development of various automated seizure detection systems that can aid neurophysiologists in accurate diagnosis of epilepsy. The present study is focused on the development of a robust automated system for classification against low levels of supervised training. EEG data from two different repositories are considered for analysis and validation of the proposed system. The signals are decomposed into time-frequency sub-bands till sixth level using dual-tree complex wavelet transform (DTCWT). All details and last approximation coefficients are used to calculate features viz. energy, standard deviation, root-mean-square, Shannon entropy, mean values and maximum peaks. These feature sets are passed through a general regression neural network (GRNN) for classification with K-fold cross-validation scheme under varying train-to-test ratios. The current model yields ceiling level classification performance (accuracy, sensitivity & specificity) in all combinations of datasets (ictal vs non-ictal) in less than 0.028s. The proposed scheme will not only maximize hit-rate and correct rejection rate but also will aid neurophysiologists in the fast and accurate diagnosis of seizure onset.

Development of pattern vision following early and extended blindness

Significance Deprivation of vision during typical age-defined critical periods results in seemingly irreversible changes in neural organization and behavior in animals and humans. We describe visual development in a unique population of patients who were blind during typical critical periods before removal of bilateral cataracts. The rarity of such cases has previously limited empirical investigations of this issue. Surprisingly, we find substantial improvement after sight onset in contrast sensitivity, a basic visual function that has well-understood neural underpinnings. Our results show that the human visual system can retain plasticity beyond critical periods, even after early and extended blindness. Visual plasticity peaks during early critical periods of normal visual development. Studies in animals and humans provide converging evidence that gains in visual function are minimal and deficits are most severe when visual deprivation persists beyond the critical period. Here we demonstrate visual development in a unique sample of patients who experienced extended early-onset blindness (beginning before 1 y of age and lasting 8–17 y) before removal of bilateral cataracts. These patients show surprising improvements in contrast sensitivity, an assay of basic spatial vision. We find that contrast sensitivity development is independent of the age of sight onset and that individual rates of improvement can exceed those exhibited by normally developing infants. These results reveal that the visual system can retain considerable plasticity, even after early blindness that extends beyond critical periods.

Results of late surgical intervention in children with early-onset bilateral cataracts.

Background Cataracts are a major cause of childhood blindness globally. Although surgically treatable, it is unclear whether children would benefit from such interventions beyond the first few years of life, which are believed to constitute ‘critical’ periods for visual development. Aims To study visual acuity outcomes after late treatment of early-onset cataracts and also to determine whether there are longitudinal changes in postoperative acuity. Methods We identified 53 children with dense cataracts with an onset within the first half-year after birth through a survey of over 20 000 rural children in India. All had accompanying nystagmus and were older than 8 years of age at the time of treatment. They underwent bilateral cataract surgery and intraocular lens implantation. We then assessed their best-corrected visual acuity 6 weeks and 6 months after surgery. Results 48 children from the pool of 53 showed improvement in their visual acuity after surgery. Our longitudinal assessments demonstrated further improvements in visual acuity for the majority of these children proceeding from the 6-week to 6-month assessment. Interestingly, older children in our subject pool did not differ significantly from the younger ones in the extent of improvement they exhibit. Conclusions and relevance Our results demonstrate that not only can significant vision be acquired until late in childhood, but that neural processes underlying even basic aspects of vision like resolution acuity remain malleable until at least adolescence. These data argue for the provision of cataract treatment to all children, irrespective of their age.

Immediate susceptibility to visual illusions after sight onset

The dominant accounts of many visual illusions are based on experience-driven development of sensitivity to certain visual cues. According to such accounts, learned associations between observed two-dimensional cues (say, converging lines) and the real three-dimensional structures they represent (a surface receding in depth) render us susceptible to misperceiving some images that are cleverly contrived to contain those two-dimensional cues. While this explanation appears reasonable, it lacks direct experimental validation. To contrast it with an account that dispenses with the need for visual experience, it is necessary to determine whether susceptibility to the illusion is present immediately after birth; however, eliciting reliable responses from newborns is fraught with operational difficulties, and studies with older infants are incapable of resolving this issue. Our work with children who gain sight after extended early-onset blindness, as part of Project Prakash, provides a potential way forward. We report here that the newly sighted children, ranging in age from 8 through 16 years, exhibit susceptibility to two well-known geometrical visual illusions, Ponzo [1] and Müller-Lyer [2], immediately after the onset of sight. This finding has implications not only for the likely explanations of these illusions, but more generally, for the nature-nurture argument as it relates to some key aspects of visual processing.

Automatic Electrooculogram Classification for Microcontroller Based Interface Design

In this paper, we present a simple and novel technique for classification of multiple channel Electrooculogram signals (EOG). In particular, a viable real time EOG signal classifier for microcontrollers is proposed. The classifier is based on Deterministic Finite Automata (DFA). The system is capable of classifying sixteen different EOG signals. The viability of the system was tested by performing online experiments with able bodied subjects.

A comparative account of modelling seizure detection system using wavelet techniques

ABSTRACTOver the last decade, application of wavelet transform (WT) has been realised for extracting features during epileptic seizure detection. Although noteworthy developments have been made in WT algorithms, most of the seizure detection works have been confined to using discrete wavelet transform (DWT). In this present effort, comparisons are made between DWT, wavelet packet transform and dual-tree complex wavelet transform (DT-CWT) for detection of epileptiform patterns in electroencephalography. For the study, combinations of energy, root-mean-square values and standard deviations are used as extracted feature inputs to the general regression neural network classifier. The paper describes unique methodology of using minimal training during K-folds cross-validation to highlight the robustness of the expert model. Classification rates, statistical parameters and computation timings are finally calculated and one-way analysis of variance is applied to validate the results. The results demonstrate stat...

Improvement in Spatial Imagery Following Sight Onset Late in Childhood

The factors contributing to the development of spatial imagery skills are not well understood. Here, we consider whether visual experience shapes these skills. Although differences in spatial imagery between sighted and blind individuals have been reported, it is unclear whether these differences are truly due to visual deprivation or instead are due to extraneous factors, such as reduced opportunities for the blind to interact with their environment. A direct way of assessing vision’s contribution to the development of spatial imagery is to determine whether spatial imagery skills change soon after the onset of sight in congenitally blind individuals. We tested 10 children who gained sight after several years of congenital blindness and found significant improvements in their spatial imagery skills following sight-restoring surgeries. These results provide evidence of vision’s contribution to spatial imagery and also have implications for the nature of internal spatial representations.